Method of recognizing human iris using daubechies wavelet transform

ABSTRACT

The present invention relates to a method of recognizing the human iris using the Daubechies wavelet transform. The dimensions of characteristic vectors are initially reduced by extracting iris features from the inputted iris image signals through the Daubechies wavelet transform. Then, the binary characteristic vectors are generated by applying quantization functions to the extracted characteristic values so that the utility of human iris recognition can be improved as the storage capacity and processing time thereof can be reduced by generating low capacity characteristic vectors. By measuring the similarity between the generated characteristic vectors and the previously registered characteristic vectors, characteristic vectors indicative of the iris patterns can be realized.

CLAIM OF PRIORITY

[0001] This application makes reference to, incorporates the sameherein, and claims all benefits accruing under 35 U.S.C. Section 119from an application for “Method of Recognizing Human Iris UsingDaubechies Wavelet Transform,” filed earlier in the Korean IndustrialProperty Office on Mar. 6, 2001, and there duly assigned Serial No.2001-11440.

BACKGROUND OF THE INVENTION

[0002] 1. Field of Invention

[0003] The present invention relates to a method of recognizing thehuman iris and, more particularly, to a method of recognizing the humaniris using the Daubechies wavelet transform to reduce the dimensions ofcharacteristic vectors to improve the processing time.

[0004] 2. Description of the Related Art

[0005] An iris recognition system is used for performing theidentification of an individual based on the information obtained fromthe analysis of the iris patterns, which are different for eachindividual. The iris recognition system has superior identificationaccuracy and thus provides excellent security when compared to otherbiometric methods that use voice and fingerprints for identification.

[0006] A wavelet transform is typically used to extract thecharacteristics of the iris images and involves analyzing signals in amulti-resolution mode. The wavelet transform is a mathematical theoryused for formulating a model for systems, signals, and a series ofprocesses using selected signals based on the Fourier transform. Thesesignals are referred to as little waves or wavelets. Recently, thewavelet transform is widely employed in the field of signal and imageprocessing as it has a faster rate when compared with the traditionalsignal processing algorithm, and it can efficiently achieve signallocalization in time and frequency domains. The images are obtained byextracting the iris patterns from an iris image that are acquired by animage acquisition device, then patterns normalized in the 450×60 sizeare used to extract the characteristic values using the wavelettransform.

[0007] There are other types of wavelet transmform known in the art. Forexample, the Harr wavelet transform has been widely used also in theconventional iris recognition systems, image processing, and the like.However, the Harr wavelet transform has disadvantages in that thecharacteristic values change irregularly and rapidly. In addition, ahigh resolution of the images cannot be obtained if the images aredecompressed again after they have been compressed. In contrast, theDaubechies wavelet transform is a continuous function, thus thedisadvantages associated with the Harr wavelet functions can be avoidedin certain instances for extracting more accurate and delicatecharacteristic values. If the images are decompressed again after theyhave been compressed using the Daubechies wavelet transform, the imagescan be restored with a high resolution quality back to the originalimages if the Harr wavelet transform is used. However, as the Daubechieswavelet functions are generally more complicated than the Harr waveletfunctions, there is a disadvantage in that a larger arithmetic quantitymay be needed. A main advantage of the Daubechies wavelet transform isthat it provides fine characteristic values when performing the wavelettransform to extract the characteristic values. That is, if theDaubechies wavelet transform is used, the identification of the irisfeatures can be made with a lower number of data, and the extraction ofthe iris features can be made accurately.

[0008] Another method of extracting the characteristic values indicativeof the iris patterns and forming the characteristic vectors uses theGabor transform. However, the characteristic vectors generated by thismethod require 256 or more dimensions and at least 256 bytes, where onebyte is assigned to one dimension. Thus, there is a problem in thatpracticability and efficiency are undermined when the Gabor transform isused in the field if low capacity information is required.

[0009] The Hamming distance (HD) is used to verify the twocharacteristic vectors generated in the form of binary vectors. Themethod of measuring a distance, such as the Hamming distance (HD)between two characteristic vectors (i.e., characteristic vectorsrelevant to the input pattern and the stored reference characteristicvectors) for the pattern classification is disclosed in U.S. Pat. No.5,291,560, the teachings of which are incorporated herein by reference.The bit values assigned according to the respective dimension arecompared with each other. If they are identical to each other, 0 isgiven; and if they are different from each other, 1 is given. Then, thevalue divided by the total number of dimensions is obtained as a finalresult. Hence, this method is simple and useful in discriminating thedegree of similarity between the characteristic vectors consisting ofbinary codes. The comparison result of all the bits becomes 0 ifidentical data are compared with each other. Thus, the resultapproaching 0 implies that the data belong to the persons themselves. Ifthe data do indeed belong to the person, the probability of the degreeof similarity will be 0.5. Accordingly, a proper limit set between 0 and0.5 will be a boundary for differentiating between people. The Hammingdistance (HD) is also excellent for application with the extracted irisfeatures by subdividing the data, but it is not suitable when lowcapacity data is to be used. If the total number of the bits of thecharacteristic vectors with 256-byte information is 2048, considerablyhigh acceptance rates are realized even though the Hamming distance isapplied. In addition, there are disadvantages in that the formation ofthe reference characteristic vectors through generalizing the patterninformation cannot be easily made, and one can not rely upon theinformation characteristics of each dimension of the characteristicvectors.

[0010] Accordingly, if the low capacity characteristic vectors are used,the accuracy of differentiating characteristic vectors is poor due to anincrease in lost information. Thus, a method of preventing informationloss while maintaining the minimum capacity of the characteristicvectors is needed in generating the characteristic vectors. Accordingly,there is a need for a method of forming the low capacity characteristicvectors, so that the processing, storage, transfer, search, and the likeof the pattern information can be achieved efficiently.

SUMMARY OF INVENTION

[0011] The present invention is directed to a method of forming lowcapacity characteristic vectors, so that the false acceptance rate (FAR)and the false rejection rate (FRR) can be remarkably reduced as comparedto the conventional Harr wavelet transform. To this end, the irisfeatures from inputted iris image signals are extracted using theDaubechies wavelet transform.

[0012] One aspect of the present invention provides a method formeasuring the similarity between the characteristic vectors, wherein thelow capacity characteristic vectors can be properly used for thesimilarity measurement while the loss of information can be minimized.

[0013] Another aspect of the present invention provides a method forrecognizing the human iris using the Daubechies wavelet transform,wherein the iris image from an eye using an image acquisition devicewith a halogen lamp illuminator is provided. The method includes thesteps of: (a) repeatedly performing the Daubechies wavelet transform ofthe iris image at predetermined times to multi-divide the iris image,and extracting an image including the high frequency components from themulti-divided image to extract iris features; (b) extracting thecharacteristic values of a characteristic vector from the extractedimage with the high frequency components, and generating a binarycharacteristic vector by quantizing the relevant characteristic values;and, (c) determining the user as an enrollee based on the similaritybetween the generated characteristic vector and a previously registeredcharacteristic vector.

[0014] According to another aspect of the present invention, the irisimage is acquired through an image acquisition device utilizing ahalogen lamp as an illuminator. By repeatedly performing the Daubechieswavelet transform of the inputted iris image, the iris image ismulti-divided, and the iris features with optimized sizes are extracted.The characteristic vector, which is effective in displaying andprocessing the image, is then formed by quantizing the extractedcharacteristic values. Furthermore, the dimension of the characteristicvector is reduced by quantizing the extracted characteristic values intobinary values—that is, when a low capacity characteristic vector isformed, the method of measuring the similarity between the weightregistered and the inputted characteristic vectors is used to preventthe reduction of acceptance resulting from the formation of the lowcapacity characteristic vector. The user authenticity is, therefore,determined by the foregoing method.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015]FIG. 1 is a view illustrating the constitution of the imageacquisition equipment used for performing an iris recognition methodaccording to the present invention.

[0016]FIG. 2 is a flowchart illustrating the process of verifying aniris image according to the present invention.

[0017]FIG. 3 is a flowchart illustrating the process of multi-dividingthe iris image using the Daubechies wavelet transform according to thepresent invention.

[0018]FIG. 4 shows an example of multi-dividing the iris image using theDaubechies wavelet transform.

[0019]FIG. 5 is a flowchart illustrating the process of forming thecharacteristic vector of an iris image based on the data acquired fromthe multi-dividing operation according to the present invention.

[0020]FIG. 6a shows a distribution example of the characteristic valuesof the extracted iris image.

[0021]FIG. 6b shows the quantization function for generating a binarycharacteristic vector from the distribution example of FIG. 6a.

[0022]FIG. 7 is a flowchart showing the procedures for determining userauthenticity through a similarity test between the characteristicvectors.

DETAILED DESCRIPTION FOR PREFERRED EMBODIMENT

[0023] Hereinafter, a method of recognizing a human iris using theDaubechies wavelet transform according to the present invention will beexplained in detail with reference to the accompanying drawings.

[0024]FIG. 1 shows the exemplary embodiment of the image acquisitionequipment for use in recognizing a human iris according to the presentinvention. The image acquisition equipment includes a halogen lamp 11for illuminating the iris in order to acquire clear iris patterns, a CCDcamera 13 for photographing the eye 10 of a user through a lens 12, aframe grabber 14 connected to the CCD camera 12 for acquiring the irisimage, and a monitor 15 for showing the image to the user so that theacquisition of correct images and the position of the user can beobtained as the images are acquired.

[0025] In the embodiment, the CCD camera 13 is used to acquire the eyeimage, and the iris recognition is made through the pattern analysis ofiridial folds. However, where the iris image is acquired indoors usingan ordinary illuminator, it is difficult to extract the desired patterninformation as the iris image is generally gloomy. Additionalilluminators should therefore be used so that the information on theiris image cannot be lost and a clear iris pattern can be obtained. Inthe present invention, the halogen lamp 11 with strong floodlightingeffects is preferably used as a main illuminator so that the irispattern can be clearly shown. However, it should be noted that otherlight sources known to those skilled in this art can be successfullyused. Furthermore, as shown in FIG. 1, the loss of the iris imageinformation and eye fatigue of the user can be avoided by placing thehalogen lamp illuminators on the left and right sides of the eye inorder to cause the reflective light from the lamp to be formed on theouter portions of the iris region.

[0026]FIG. 2 is a flowchart showing the operation steps for verifyingthe iris image for identification purposes according to the presentinvention. Referring to FIG. 2, the eye image is acquired through theimage acquisition equipment shown in FIG. 1 in step 200. In step 210,the images of the iris regions are extracted from the acquired eye imagethrough pre-processing and transformed into a polar coordinate system,then the transformed iris pattern is inputted to a module for extractingthe features. Acquiring the iris image and transforming the image into apolar coordinate system are well known in the art that can be performedin a variety of ways. In step 220, the Daubechies wavelet transform ofthe inputted iris pattern transformed into the polar coordinate systemis performed, and the features of the iris regions are then extracted.The extracted features would have real numbers. In step 230, a binarycharacteristic vector is generated by applying a K-level quantizationfunction to the extracted features. In step 240, the similarity betweenthe generated characteristic vector and the previously registered dataof the user is measured. Through the similarity measurement, userauthenticity is determined and then the verification results areobtained.

[0027] In a case where the features of the iris regions are extracted byperforming the Daubechies wavelet transform as described above, theDaubechies wavelet function with eight, sixteen, or more coefficientscan extract more delicate characteristic values than the Daubechieswavelet function with four coefficients, even though the former methodis more complicated than the latter. Although the Daubechies waveletfunction with eight or more coefficients has been used and tested in thepresent invention, greater performance improvement was not obtained andthe arithmetic quantity and processing time are increased, as comparedwith a case where the Daubechies wavelet function with four coefficientsis tested. Hence, the Daubechies wavelet function with four coefficientsmay be used for extracting the characteristic values indicative of theiris patterns.

[0028]FIG. 3 is a flowchart showing the process of multi-dividing theiris image by performing the Daubechies wavelet transform according tothe present invention. FIG. 4 shows an image divided using theDaubechies wavelet transform. As shown in FIG. 4, when “L” and “H” arerespectively used to indicated low frequency and high frequencycomponents, the term “LL” indicates the component that has passedthrough a low-pass filter (LPF) in all x and y directions, whereas theterm “HH” indicates the component that has passed through a high-passfilter (HPF) in the x and y directions. The subscript numerals signifyimage-dividing stages. For example, “LH₂” means that the image haspassed through the low-pass filter in the x direction and through thehigh-pass filter in the y direction during the 2-stage wavelet division.

[0029] Referring back to FIG. 3, in step 310, the inputted iris image ismulti-divided using the Daubechies wavelet transform. As the iris imageis considered a two-dimensional signal in which one-dimensional signalsare arrayed in the x and y directions, quarterly divided components ofone image should be extracted by passing through the LPF and HPF in allx and y directions in order to analyze the iris image. That is, onetwo-dimensional image signal is wavelet-transformed in vertical andhorizontal directions, and the image is divided into four regions: LL,LH, HL, and HH after the wavelet transform has been performed once. Atthis time, using the Daubechies wavelet transform, the signal is dividedinto a differential component thereof that has passed through thehigh-pass filter and an average component that has passed through thelow-pass filter.

[0030] The performance of the iris recognition system is evaluated inview of two factors; a false acceptance rate (FAR) and a false rejectionrate (FRR). Here, the FAR means the probability that the entrance ofunregistered persons (imposters) may be accepted due to the falserecognition of unregistered persons as registered persons, and the FRRmeans the probability that entrance of registered persons (enrollees) isrejected due to false recognition of the registered persons asunregistered ones. In simulation, when the method of recognizing thehuman iris using the Daubechies wavelet transform according to thepresent invention was employed, the FAR has been reduced from 5.5% to3.07% and the FRR has also been reduced from 5.0% to 2.25%, as comparedwith the method of recognizing the human iris using the conventionalHarr wavelet transform.

[0031] In step 320, a region HH including only the high frequencycomponents in the x and y directions are extracted from the divided irisimage.

[0032] In step 330, after increasing the iterative number of times ofdividing the iris image, the processing step is completed when theiterative number is greater than a predetermined number. Alternatively,if the iterative number is lower than the predetermined number, theinformation on the region HH is stored for use in extracting the irisfeatures in step 340.

[0033] In step 350, the region LL comprising only low frequencycomponents in the x and y directions is extracted from the multi-dividediris image. As the extracted region LL (corresponding to the imagereduced in a fourth size as compared with the previous image) includesmajor information on the iris image, it is provided as an image to benewly processed so that the wavelet transform can be applied again tothe relevant region. Thereafter, the Daubechies wavelet transform isrepeated again from step 310.

[0034] In a case where the iris image is transformed from the Cartesiancoordinate system to the polar coordinate system, in order to avoidchanges in the iris features according to variations in the size of thepupil, the region between the inner and outer boundaries of the iris isdivided into 60 segments in the r direction and 450 segments in the θdirection by varying the angles by 0.8 degrees. Finally, the informationon the iris image is acquired and normalized as 450×60 (θ×r) data. Then,if the acquired iris image is once again wavelet-transformed, thecharacteristics of the 225×30 region HH₁ of which size is reduced byhalf are obtained, namely, the 225×30 information is used as acharacteristic vector. This information may be used as it is, but theprocess of dividing the signals is repeatedly performed in order toreduce the information size. Since the region LL includes majorinformation on the iris image, the characteristic values of furtherreduced regions, such as HH₂, HH₃, and HH₄, are obtained by successivelyapplying the wavelet transform to the respective relevant regions.

[0035] The iterative number, which is provided as a discriminatingcriterion for repeatedly performing the wavelet transform, should be setas an optimal value in consideration of the loss of the information andthe size of the characteristic vector. Therefore, in the presentinvention, the region HH₄ obtained by performing the wavelet transformfour times becomes a major characteristic region, and the values thereofare selected as the components of the characteristic vector. At thistime, the region HH₄ contains the information having 84 (=28×3) data.

[0036]FIG. 5 is a flowchart showing the process of forming thecharacteristic vector of the iris image using the data acquired from themulti-divided iris image according to the present invention. Referringto FIG. 5, the information on the n characteristic vector extracted fromthe above process, i.e., the information on the regions HH₁, HH₂, HH₃,and HH₄ is inputted in step 510. In step 520, in order to acquire thecharacteristic information on the regions HH₁, HH₂, and HH₃ excludingthe information on the region HH₄ obtained through the last wavelettransform among the n characteristic vector, each average value of theregions HH₁, HH₂, and HH₃ is calculated and assigned one dimension. Instep 530, all values of the final obtained region HH₄ are extracted asthe characteristic values thereof. After extraction of thecharacteristics of the iris image signals has been completed, thecharacteristic vector is generated based on these characteristics. Amodule for generating the characteristic vector mainly performs theprocesses of extracting the characteristic values in the form of realnumbers and then transforming them to binary codes consisting of 0 and1.

[0037] However, in step 540, the N−1 characteristic values extractedfrom step 520 and the M (the size of the final obtained region HH)characteristic values extracted from step 530 are combined and(M+N−1)-dimensional characteristic vector is generated. That is, thetotal 87 data, which the 84 data of the region HH₄ and the 3 averagedata of the regions HH₁, HH₂, and HH₃ are combined, are used as acharacteristic vector in the present invention.

[0038] In step 550, the values of the previously obtained characteristicvector, i.e., the respective component values of the characteristicvector expressed in the form of the real numbers, are quantized intobinary values 0 or 1. In step 560, the resultant (M+N−1)-bitcharacteristic vector is generated by the quantized values. That is,according to the present invention, the resultant 87-bit characteristicvector is generated.

[0039]FIG. 6a shows a distribution example of the characteristic valuesof the extracted iris image. When the values of the 87-dimensionalcharacteristic vector are distributed according to the respectivedimensions, the distribution roughly takes the shape of FIG. 6a. Thebinary vector including all the dimensions is generated by the followingEquation 1.

f _(n)=0iff(n)<0

f _(n)=1 if f(n)>0  (1),

[0040] where f(n) is a characteristic value of the n-th dimension, andf_(n) is the value of the n-th characteristic vector

[0041] When the 87-bit characteristic vector that is obtained byassigning one bit to the total 87 dimensions are generated in order touse a low capacity characteristic vector, the improvement of therecognition rate is limited to some extent as loss of the information onthe iris image is increased. Therefore, when generating thecharacteristic vector, it is necessary to prevent information loss whilemaintaining the minimum capacity of the characteristic vector.

[0042]FIG. 6b shows a quantization function for generating a binarycharacteristic vector from the distribution example of thecharacteristic values shown in FIG. 6a. The extracted(M+N−1)-dimensional characteristic vector shown in FIG. 6a is evenlydistributed mostly between 1 and −1 in view of its magnitude. Then, thebinary vector is generated by applying the K-level quantization functionshown in FIG. 6a to the characteristic vector. Since only signs of thecharacteristic values are obtained through the process of Equation 1, itis understood that information on the magnitude has been discarded.Thus, in order to accept the magnitude of the characteristic vector, a4-level quantization process was utilized in the present invention.

[0043] As described above, in order to efficiently compare thecharacteristic vector generated through the 4-level quantization withthe registered characteristic vector, the quantization levels have theweights expressed in the following Equation 2.

f _(n)=4 if f(n)≧0.5 (level 4)

f _(n)=1 if 0.5>f(n)≧0 (level 3)

f _(n)=−1 if 0>f(n)>−0.5 (level 2)

f _(n)=−4 if f(n)≦−0.5 (level 1)  (2),

[0044] where f_(n) represents the n-th dimension of the previouslyregistered characteristic vector f_(R) of the user or the characteristicvector f_(T) of the user generated from the iris image of the eye imageof the user. An explanation of how to use the weights expressed inEquation 2, is as follows.

[0045] In a case where the n-th dimensional characteristic value f(n) isequal or more than 0.5 (level 4), the value of the i-th dimension f_(Ri)or f_(Ti) is converted and assigned “4” if the value is “11.” In a casewhere the n-th dimensional characteristic value f(n) is more than 0 andless than 0.5 (level 3), the value of the i-th dimension f_(Ri) orf_(Ti) is converted and assigned “1” if the value is “10.” In a casewhere the n-th dimensional characteristic value f(n) is more than −0.5and less than 0 (level 2), the value of the i-th dimension f_(Ri) orf_(Ti) is converted and assigned −1 if the value is “01.” In a casewhere the n-th dimensional characteristic value f(n) is equal to or lessthan −0.5 (level 1), the value of the i-th dimension f_(Ri) or f_(Ti) isconverted and assigned −4 if the value is “00.” This is due to theweights being applied to the respective values as expressed in Equation2 as it is suitable for the following verification method of the presentinvention.

[0046]FIG. 7 is a flowchart showing the procedures for discriminatinguser authenticity through the similarity measurement test between thecharacteristic vectors. Referring to FIG. 7, in step 710, thecharacteristic vector f_(T) of the user is generated from the iris imageof the eye image of the user. Step 720, searches the previouslyregistered characteristic vector f_(R) of the user. In step 730, inorder to measure the similarity between the two characteristic vectors,the weights are assigned to the characteristic vectors f_(R) and f_(T)depending on the value of the binary characteristic vector based onEquation 2.

[0047] In step 740, an inner product or scalar product S of the twocharacteristic vectors is calculated and the similarity is finallymeasured. Among the measures generally used for determining thecorrelation between the registered characteristic vector f_(R) and thecharacteristic vector f_(T) of the user, it is the inner product S ofthe two characteristic vectors that indicate the most directassociation. That is, after the weights have been assigned to therespective data of the characteristic vector in step 730, the innerproduct S of the two characteristic vectors is used to measure thesimilarity between the two vectors.

[0048] The following Equation 3 is used for calculating the innerproduct of the two characteristic vectors. $\begin{matrix}{{S = {{\sum\limits_{i = 1}^{n}{f_{Ri}f_{Ti}}} = ( {{f_{R1}f_{T1}} + {f_{R2}f_{T2}} + \ldots + {f_{Rn}f_{Tn}}} )}},} & (3)\end{matrix}$

[0049] where f_(R) is the characteristic vector of the user that hasbeen already registered, and f_(T) is the characteristic vector of theuser that is generated from the iris image of the eye of the user.

[0050] According to the above processes, one effect, which can beobtained by the quantization according to the sign of the characteristicvector values as in the method in which the binary vector, is generatedwith respect to the values of the characteristic vector extracted fromthe iris image according to the respective dimensions. That is, like theHamming distance, the difference between 0 and 1 can be expressed. In acase where the two characteristic vectors have the same-signed valueswith respect to each dimension, positive values are added to the innerproduct S of the two characteristic vectors. Otherwise, negative valuesare added to the inner product S of the two vectors. Consequently, theinner product S of the two characteristic vectors increases if the twodata belong to an identical person, while the inner product S of the twocharacteristic vectors decreases if the two data do not belong to anidentical person.

[0051] In step 750, the user authenticity is determined according to themeasured similarity obtained from the inner product S of the twocharacteristic vectors. At this time, the determination of the userauthenticity based on the measured similarity depends on the followingEquation 4.

If S>C, then TRUE or else FALSE  (4),

[0052] where C is a reference value for verifying the similarity betweenthe two characteristic vectors.

[0053] That is, if the inner product S of the two characteristic vectorsis equal to or more than the verification reference value C, the user isdetermined as an enrollee. Otherwise, the user is determined as animposter.

[0054] As described above, the method of recognizing the human irisusing the Daubechies wavelet transform according to the presentinvention has an advantage in that FAR and FRR can be remarkably reducedas compared with the method using the conventional Harr wavelettransform, as the iris features are extracted from the inputted irisimage signals through the Daubechies wavelet transform.

[0055] Furthermore, in order to verify the similarity between theregistered and extracted characteristic vectors f_(R) and f_(T), theinner product S of the two characteristic vectors is calculated, and theuser authenticity is determined based on the measured similarityobtained by the calculated inner product S of the two vectors.Therefore, there is provided a method of measuring the similaritybetween the characteristic vectors wherein the loss of the information,which may be produced by forming the low capacity characteristicvectors, can be minimized.

[0056] The foregoing is a mere embodiment for embodying the method ofrecognizing the human iris using the Daubechies wavelet transformaccording to the present invention. However, the present invention isnot limited to the embodiment described above. A person skilled in theart can make various modifications and changes to the present inventionwithout departing from the technical spirit and the scope of the presentinvention defined by the appended claims.

What is claimed is:
 1. A method of recognizing a human iris using theDaubechies wavelet transform, the method comprising the steps of: (a)obtaining an iris image from a user's eye using an image acquisitiondevice; (b) repeatedly performing said Daubechies wavelet transform onsaid iris image so as to multi-divide said iris image for apredetermined number of times; (c) extracting image with high frequencycomponents from said multi-divided image so as to extract iris features;(d) extracting characteristic values of a characteristic vector fromsaid extracted image with said high frequency components; (e) generatinga binary characteristic vector by quantizing said extractedcharacteristic values; and, (f) determining whether said user as anenrollee by measuring a similarity between said generated characteristicvector and a previously registered characteristic vector.
 2. The methodof claim 1, further comprising the step of illuminating said user's eye.3. The method of claim 2, wherein the step of illuminating said user'seye comprises the step of placing a halogen lamp at both ends of saiduser's eye.
 4. The method of claim 1, wherein said step (b) comprisesthe steps of: extracting a region HH from said multi-divided imagehaving said high frequency components in both x and y directions;storing information of said region HH for use in extracting irisfeatures; performing multi-division of a region LL from saidmulti-divided image having low frequency components in both x and ydirections.
 5. The method of claim 2, wherein said predetermined numberof times is set at four.
 6. The method of claim 1, wherein said step (c)comprises the steps of: receiving multi-divided images of a plurality ofhigh frequency regions HH_(i) formed by said multi-division in said step(b); calculating the average values of regions HH₁ to HH_(n−1) excludingthe last region HH_(N); assigning said calculated average values to thecomponents of said characteristic vector, respectively; assigning saidcalculated value M of said last region HH_(N) to the components of saidbinary characteristic vector; combining said N−1 average values and saidM values so as to generate a (M+N−1)-dimensional characteristic vector;and, quantizing all values of said generated characteristic vector intobinary values so as to generate a final (M+N−1)-dimensionalcharacteristic vector.
 7. The method of claim 1, wherein said step (f)comprises the steps of: applying predetermined weights to the i-thdimensions of said generated characteristic vector generated from saidstep (c) and said previously registered characteristic vector;calculating the inner product S of said two weighted characteristicvectors; and determining said user as an enrollee if said inner productS is more than a verification reference value C.
 8. The method of claim1, wherein said image acquisition device comprises a halogen lamp.